SoroushVahidi/scidocs huggingface dataset:
Improved Shortest Path Maps with GPU Shaders
(2018)

We present in this paper several improvements for computing shortest path maps using OpenGL shaders [1]. The approach explores GPU rasterization as a way to propagate optimal costs on a polygonal 2D environment, producing shortest path maps which can efficiently be queried at run-time. Our improved method relies on Compute Shaders for improved performance, does not require any CPU pre-computation, and handles shortest path maps both with source points and with line segment sources. The produced path maps partition the input environment into regions sharing a same parent point along the shortest path to the closest source point or segment source. Our method produces paths with global optimality, a characteristic which has been mostly neglected in animated virtual environments. The proposed approach is particularly suitable for the animation of multiple agents moving toward the entrances or exits of a virtual environment, a situation which is efficiently represented with the proposed path maps.
The Cyclic Towers of Hanoi
(1981)

The famous Towers of Hanoi puzzle consists of 3 pegs (A, B, C) on one of which (A) are stacked n rings of different sizes, each ring resting on a larger ring. The objective is to move the n rings one by one until they are all stacked on another peg (B) in such a way that no ring is ever placed on a smaller ring; the other peg (C) can be used as workspace. The problem has tong been a favourite iir programming courses as one which admits a concise recursive solution. This solution hinges on the observation that, when the largest ring is moved from A to B, the n 1 remaining rings must all be on peg C. This immediately leads to the recursive procedure
Shakeout: A New Regularized Deep Neural Network Training Scheme
(2016)

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights. In this paper, we present a new training scheme: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, our method randomly chooses to enhance or inverse the contributions of each unit to the next layer. We show that our scheme leads to a combination of L1 regularization and L2 regularization imposed on the weights, which has been proved effective by the Elastic Net models in practice. We have empirically evaluated the Shakeout scheme and demonstrated that sparse network weights are obtained via Shakeout training. Our classification experiments on real-life image datasets MNIST and CIFAR10 show that Shakeout deals with over-fitting effectively.
Big Data Security: Survey on Frameworks and Algorithms
(2017)

Technology today has progressed to an extent wherein collection of data is possible for every granular aspect of a business, in real time. Electronic devices, power grids and modern software all generate huge volumes of data, which is in the form of petabytes, exabytes and zettabytes. It is important to secure existing big data environments due to increasing threats of breaches and leaks from confidential data and increased adoption of cloud technologies due to the ability of buying processing power and storage on-demand. This is exposing traditional and new data warehouses and repositories to the outside world and at the risk of being compromised to hackers and malicious outsiders and insiders. In this paper the current big data scenario has been summarized along with challenges faced and security issues that need attention. Also some existing approaches have been described to illustrate current and standard directions to solving the issues.
Scribbles to Vectors: Preparation of Scribble Drawings for CAD Interpretation
(2007)

This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled drawings are converted into a vectorial format which can be recognized by a CAD system. This is achieved by using pattern analysis techniques, namely the Gabor filter to simplify the scribbled drawing. Vector line are then extracted from the resulting drawing by means of Kalman filtering.
On the Robust Control of Buck-Converter DC-Motor Combinations
(2013)

The concepts of active disturbance rejection control and flatness-based control are used in this paper to regulate the response of a dc-to-dc buck power converter affected by unknown, exogenous, time-varying load current demands. The generalized proportional integral observer is used to estimate and cancel the time-varying disturbance signals. A key element in the proposed control for the buck converter-dc motor combination is that even if the control input gain is imprecisely known, the control strategy still provides proper regulation and tracking. The robustness of this method is further extended to the case of a double buck topology driving two different dc motors affected by different load torque disturbances. Simulation results are provided.
Comparative study of Proportional Integral and Backstepping controller for Buck converter
(2011)

This paper describes the comparative study of Proportional Integral (PI) and Backstepping controller for Buck converter with R-load and DC motor. Backstepping approach is an efficient control design procedure for both regulation and tracking problems. This approach is based upon a systematic procedure which guarantees global regulation and tracking. The proposed control scheme is to stabilize the output (voltage or speed) and tracking error to converge zero asymptotically. Buck converter system is simulated in MATLAB, using state reconstruction techniques. Simulation results of buck converter with R-load and PMDC motor reveals that, settling time of Backstepping controller is less than PI controller
Robust Passivity-Based Control of a Buck–Boost-Converter/DC-Motor System: An Active Disturbance Rejection Approach
(2012)

This paper presents an active disturbance rejection (ADR) approach for the control of a buck-boost-converter feeding a dc motor. The presence of arbitrary, time-varying, load torque inputs on the dc motor and the lack of direct measurability of the motor's angular velocity variable prompts a generalized proportional integral (GPI) observer-based ADR controller which is synthesized on the basis of passivity considerations. The GPI observer simultaneously estimates the angular velocity and the exogenous disturbance torque input in an on-line cancellation scheme, known as the ADR control. The proposed control scheme is thus a sensorless one with robustness features added to the traditional energy shaping plus damping injection methodology. The discrete switching control realization of the designed continuous feedback control law is accomplished by means of a traditional PWM-modulation scheme. Additionally, an input to state stability property of the closed-loop system is established. Experimental and simulation results are provided.
Closed-Loop Analysis and Cascade Control of a Nonminimum Phase Boost Converter
(2011)

In this paper, a cascade controller is designed and analyzed for a boost converter. The fast inner current loop uses sliding-mode control. The slow outer voltage loop uses the proportional-integral (PI) control. Stability analysis and selection of PI gains are based on the nonlinear closed-loop error dynamics. It is proven that the closed-loop system has a nonminimum phase behavior. The voltage transients and reference voltage are predictable. The current ripple and system sensitivity are studied. The controller is validated by a simulation circuit with nonideal circuit parameters, different circuit parameters, and various maximum switching frequencies. The simulation results show that the reference output voltage is well tracked under parametric changes, system uncertainties, or external disturbances with fast dynamic transients, confirming the validity of the proposed controller.
A general approach to control a Positive Buck-Boost converter to achieve robustness against input voltage fluctuations and load changes
(2008)

A positive buck-boost converter is a known DC- DC converter which may be controlled to act as buck or boost converter with same polarity of the input voltage. This converter has four switching states which include all the switching states of the above mentioned DC-DC converters. In addition there is one switching state which provides a degree of freedom for the positive buck-boost converter in comparison to the buck, boost, and inverting buck-boost converters. In other words the positive buck- boost converter shows a higher level of flexibility for its inductor current control compared to the other DC-DC converters. In this paper this extra degree of freedom is utilised to increase the robustness against input voltage fluctuations and load changes. To address this capacity of the positive buck-boost converter, two different control strategies are proposed which control the inductor current and output voltage against any fluctuations in input voltage and load changes. Mathematical analysis for dynamic and steady state conditions are presented in this paper and simulation results verify the proposed method.
Wideband Millimeter-Wave Surface Micromachined Tapered Slot Antenna
(2014)

A millimeter-wave surface micromachined tapered slot antenna (TSA) fed by an air-filled rectangular coaxial line is proposed. Detailed parametric study is conducted to determine the effects of the TSA's key structural features on its VSWR and gain. Selected exponential taper with determined growth rate and corrugation length resulted in an antenna with VSWR <; 2.5, gain > 2.75 dBi, and stable patterns over a 43-140-GHz range. The TSA is fabricated, and good correlation between modeling and W-band measurements confirms its wideband performance.
The genomic landscapes of human breast and colorectal cancers.
(2007)

Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.
Incorporating partitioning and parallel plans into the SCOPE optimizer
(2010)

Massive data analysis on large clusters presents new opportunities and challenges for query optimization. Data partitioning is crucial to performance in this environment. However, data repartitioning is a very expensive operation so minimizing the number of such operations can yield very significant performance improvements. A query optimizer for this environment must therefore be able to reason about data partitioning including its interaction with sorting and grouping. SCOPE is a SQL-like scripting language used at Microsoft for massive data analysis. A transformation-based optimizer is responsible for converting scripts into efficient execution plans for the Cosmos distributed computing platform. In this paper, we describe how reasoning about data partitioning is incorporated into the SCOPE optimizer. We show how relational operators affect partitioning, sorting and grouping properties and describe how the optimizer reasons about and exploits such properties to avoid unnecessary operations. In most optimizers, consideration of parallel plans is an afterthought done in a postprocessing step. Reasoning about partitioning enables the SCOPE optimizer to fully integrate consideration of parallel, serial and mixed plans into the cost-based optimization. The benefits are illustrated by showing the variety of plans enabled by our approach.
Sentiment classification of Hinglish text
(2016)

In order to determine the sentiment polarity of Hinglish text written in Roman script, we experimented with different combinations of feature selection methods and a host of classifiers using term frequency-inverse document frequency feature representation. We carried out in total 840 experiments in order to determine the best classifiers for sentiment expressed in the news and Facebook comments written in Hinglish. We concluded that a triumvirate of term frequency-inverse document frequency-based feature representation, gain ratio based feature selection, and Radial Basis Function Neural Network as the best combination to classify sentiment expressed in the Hinglish text.
Multi words quran and hadith searching based on news using TF-IDF
(2016)

Each week religious leaders need to give advice to their community. Religious advice matters ideally contain discussion and solution of the problem that arising in society. But the lot of religious resources that must be considered agains many arising problems make this religious task is not easy. Especially in moslem community, the religious resources are Quran and Kutubus Sitah, the six most referenced collection of Muhammad (pbuh) news (hadith). The problem that arising in society can be read from various online mass media. Doing manually, they must know the Arabic word of the problem, and make searching manually from Mu'jam, Quran and Hadith index, then write out the found verses or hadith. TF-IDF method is often used in the weighting informational retrieval and text mining. This research want to make tools that get input from mass media news, make multi words searching from database using TF-IDF (Term Frequency - Inverse Document Frequency), and give relevan verse of Quran and hadith. Top five the most relevan verse of Quran and hadith will be displayed. Justified by religious leader, application give 60% precision for Quranic verses, and 53% for hadith, with the average query time 2.706 seconds.
GALE: Geometric Active Learning for Search-Based Software Engineering
(2015)

Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.
Local force cues for strength and stability in a distributed robotic construction system
(2017)

Construction of spatially extended, self-supporting structures requires a consideration of structural stability throughout the building sequence. For collective construction systems, where independent agents act with variable order and timing under decentralized control, ensuring stability is a particularly pronounced challenge. Previous research in this area has largely neglected considering stability during the building process. Physical forces present throughout a structure may be usable as a cue to inform agent actions as well as an indirect communication mechanism (stigmergy) to coordinate their behavior, as adding material leads to redistribution of forces which then informs the addition of further material. Here we consider in simulation a system of decentralized climbing robots capable of traversing and extending a two-dimensional truss structure, and explore the use of feedback based on force sensing as a way for the swarm to anticipate and prevent structural failures. We consider a scenario in which robots are tasked with building an unsupported cantilever across a gap, as for a bridge, where the goal is for the swarm to build any stable spanning structure rather than to construct a specific predetermined blueprint. We show that access to local force measurements enables robots to build cantilevers that span significantly farther than those built by robots without access to such information. This improvement is achieved by taking measures to maintain both strength and stability, where strength is ensured by paying attention to forces during locomotion to prevent joints from breaking, and stability is maintained by looking at how loads transfer to the ground to ensure against toppling. We show that swarms that take both kinds of forces into account have improved building performance, in both structured settings with flat ground and unpredictable environments with rough terrain.
Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks
(2009)

Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optimal set of sensors as well as use of learning mechanisms and Artificial Intelligence (AI) techniques. They have only made some assumptions on what might be considered as appropriate sensor or an arbitrary AI technique has been used. By closing the gap between traditional fire detection techniques and modern wireless sensor network capabilities, in this paper we present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection. Additionally, applicability of a feed forward neural network (FFNN) and Naïve Bayes Classifier is investigated and results in terms of detection rate and computational complexity are analyzed.
Towards Multi-Agent Communication-Based Language Learning
(2016)

We propose an interactive multimodal framework for language learning. Instead of being passively exposed to large amounts of natural text, our learners (implemented as feed-forward neural networks) engage in cooperative referential games starting from a tabula rasa setup, and thus develop their own language from the need to communicate in order to succeed at the game. Preliminary experiments provide promising results, but also suggest that it is important to ensure that agents trained in this way do not develop an adhoc communication code only effective for the game they are playing.
A region-based image caption generator with refined descriptions
(2018)

Describing the content of an image is a challenging task. To enable detailed description, it requires the detection and recognition of objects, people, relationships and associated attributes. Currently, the majority of the existing research relies on holistic techniques, which may lose details relating to important aspects in a scene. In order to deal with such a challenge, we propose a novel region-based deep learning architecture for image description generation. It employs a regional object detector, recurrent neural network (RNN)-based attribute prediction, and an encoderdecoder language generator embedded with two RNNs to produce refined and detailed descriptions of a given image. Most importantly, the proposed system focuses on a local based approach to further improve upon existing holistic methods, which relates specifically to image regions of people and objects in an image. Evaluated with the IAPR TC-12 dataset, the proposed system shows impressive performance, and outperforms state-of-the-art methods using various evaluation metrics. In particular, the proposed system shows superiority over existing methods when dealing with cross-domain indoor scene images.
3D printing: Basic concepts mathematics and technologies
(2013)

3D printing is the process of being able to print any object layer by layer. But if we question this proposition, can we find any three dimensional objects that can't be printed layer by layer? To banish any disbeliefs we walked together through the mathematics that prove 3d printing is feasible for any real life object. 3d printers create three dimensional objects by building them up layer by layer. The current generation of 3d printers typically requires input from a CAD program in the form of an STL file, which defines a shape by a list of triangle vertices. The vast majority of 3d printers use two techniques, FDM (Fused Deposition Modelling) and PBP (Powder Binder Printing). One advanced form of 3d printing that has been an area of increasing scientific interest the recent years is bioprinting. Cell printers utilizing techniques similar to FDM were developed for bioprinting. These printers give us the ability to place cells in positions that mimic their respective positions in organs. Finally through series of case studies we show that 3d printers in medicine have made a massive breakthrough lately.
3D strip meander delay line structure for multilayer LTCC-based SiP applications
(2008)

Recently, the timing control of high-frequency signals is strongly demanded due to the high integration density in three-dimensional (3D) LTCC-based SiP applications. Therefore, to control the skew or timing delay, new 3D delay lines will be proposed. For frailty of the signal via, we adopt the concept of coaxial line and proposed an advanced signal via structure with quasi coaxial ground (QCOX-GND) vias. We will show the simulated results using EM and circuit simulator.
Visual secret sharing scheme for (k, n) threshold based on QR code with multiple decryptions
(2017)

In this paper, a novel visual secret sharing (VSS) scheme based on QR code (VSSQR) with (k, n) threshold is investigated. Our VSSQR exploits the error correction mechanism in the QR code structure, to generate the bits corresponding to shares (shadow images) by VSS from a secret bit in the processing of encoding QR. Each output share is a valid QR code that can be scanned and decoded utilizing a QR code reader, which may reduce the likelihood of attracting the attention of potential attackers. Due to different application scenarios, two different recovered ways of the secret image are given. The proposed VSS scheme based on QR code can visually reveal secret image with the abilities of stacking and XOR decryptions as well as scan every shadow image, i.e., a QR code, by a QR code reader. The secret image could be revealed by human visual system without any computation based on stacking when no lightweight computation device. On the other hand, if the lightweight computation device is available, the secret image can be revealed with better visual quality based on XOR operation and could be lossless revealed when sufficient shares are collected. In addition, it can assist alignment for VSS recovery. The experiment results show the effectiveness of our scheme.